Arctic charr brain transcriptome strongly affected by summer seasonal growth but only subtly by feed deprivation

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R E S E A R C H A R T I C L E Open Access

Arctic charr brain transcriptome strongly affected by summer seasonal growth but only subtly by feed deprivation

Anja Striberny^1* , Even H. Jørgensen¹, Christophe Klopp²and Elodie Magnanou³

Abstract

Background:The Arctic charr (Salvelinus alpinus) has a highly seasonal feeding cycle that comprises long periods of voluntary fasting and a short but intense feeding period during summer. Therefore, the charr represents an interesting species for studying appetite-regulating mechanisms in fish.

Results:In this study, we compared the brain transcriptomes of fed and feed deprived charr over a 4 weeks trial during their summer feeding season. Despite prominent differences in body condition between fed and feed deprived charr at the end of the trial, feed deprivation affected the brain transcriptome only slightly. In contrast, the transcriptome differed markedly over time in both fed and feed deprived charr, indicating strong shifts in basic cell metabolic processes possibly due to season, growth, temperature, or combinations thereof. The GO enrichment analysis revealed that many biological processes appeared to change in the same direction in both fed and feed deprived fish. In the feed deprived charr processes linked to oxygen transport and apoptosis were down- and up-regulated, respectively. Known genes encoding for appetite regulators did not respond to feed deprivation. Gene expression ofDeiodinase 2 (DIO2), an enzyme implicated in the regulation of seasonal processes in mammals, was lower in response to season and feed deprivation. We further found a higher expression ofVGF(non-acronymic) in the feed deprived than in the fed fish. This gene encodes for a neuropeptide associated with the control of energy metabolism in mammals, and has not been studied in relation to regulation of appetite and energy homeostasis in fish.

Conclusions:In the Arctic charr, external and endogenous seasonal factors for example the increase in temperature and their circannual growth cycle, respectively, evoke much stronger responses in the brain than 4 weeks feed deprivation. The absence of a central hunger response in feed deprived charr give support for a strong resilience to the lack of food in this high Arctic species. DIO2 and VGF may play a role in the regulation of energy homeostasis and need to be further studied in seasonal fish.

Keywords: Feed deprivation, Salvelinus alpinus, RNA-seq, Brain transcriptome, Neuropeptides, Season

Background

Feeding is pivotal for animals in order to sustain their energy and substrate needs to live, grow and reproduce.

In mammals, energy intake and expenditure are tightly regulated by a crosstalk of peripheral and central signalling actors and pathways [1]. Peripherally derived hunger (orexigenic) and satiety (anorexigenic) signals as well as long-term signals reporting energy status are perceived

and processed in a number of brain nuclei in order to control short-term (meal-to-meal) appetite and long-term energy homeostasis [2, 3]. Of these, the arcuate nucleus (ARC) in the hypothalamus represents the pivot for controlling food intake and energy balance [4]. The ARC contains two populations of“first order”neurons, one ex- pressing the anorexigenic proopiomelanocortin (POMC) and cocaine-and amphetamine regulated transcript (CART), the other the orexigenic agouti-related peptide (AgRP) and neuropeptide Y (NPY) [3]. These project to

“second order”neurons that transduce orexigenic and anorexigenic signals via NPY and melanocortin receptors

© The Author(s). 2019Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

* Correspondence:[email protected]

1Department of Arctic and Marine Biology, UiT–The Arctic University of Norway, Tromsø, Norway

Full list of author information is available at the end of the article

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(MCR) [1]. While NPY signalling through its receptors causes an orexigenic response, signalling through MCRs results in either an anorexigenic or an orexigenic response. POMC-derived α-melanocyte-stimulating hormone (α-MSH) is a melanocortin 4 receptor (MC4R) agonist and a potent appetite suppressor in mammals [5].

AgRP, on the other hand, is an inverse agonist to the con- stitutively active MCRs and increases food intake [6].

These appetite and energy signalling neuropeptides have been shown to be evolutionary conserved [7,8] and to be involved in the control of food intake in fish [9,10]. How- ever, responses of central appetite regulators to energy perturbation vary across species and even within species depending on the experimental design [11]. For example, NPY expression is higher after 7 days of feed deprivation in the hypothalamus of zebrafish (Danio rerio) [12] and in the preoptic area of chinook salmon (Oncorhynchus tsha- wytscha) and coho salmon (Oncorhynchus kisutch) [13], whereas in cunner (Tautogolabrus adsperus), hypothal- amicNPYexpression remains unaffected after 7 days feed deprivation [14]. Such differences in the response to feed deprivation are not unexpected as fish represent the most diverse group of vertebrates with a myriad of adaptations to spatially different and temporal changing environments.

For example, in the high-latitude inhabiting anadromous (sea-migrating) Arctic charr (Salvelinus alpinus), food intake varies dramatically from little or no feeding while res- iding in fresh water during winter to voracious feeding during their short summer residence in the sea [15, 16].

This behaviour, which presumably developed as a response to seasonal and spatial differences in water temperature and food availability at high latitudes, now appears to be regulated independently of these factors;

captive offspring of anadromous Arctic charr exhibit pro- nounced seasonal changes in appetite and growth when held at constant temperature and given food in excess [17]. Despite such seasonal changes in food intake, the expressions of orexigenic and anorexigenic appetite regulators in different brain regions is similar between anorexic winter charr with hyperphagic summer charr [18]. Fur- thermore, the expressions of orexigenic and anorexigenic neuropeptides in the hypothalamus are unaffected by short- and long-term feed deprivation in this species [19].

However, in these and in most other studies investigating appetite regulation in fish, expression levels of known appetite regulators have been measured by RT-qPCR, a method with the drawback of restricting the focus to a limited number of genes. The fact that novel actors in the complex control of food intake are still being discovered calls for a more global approach when investigating appetite regulation in fish. Today, high-throughput RNA se- quencing is a powerful tool in experimental biology.

Transcriptomic approaches have been applied in various contexts to improve knowledge of the biology of the

seasonal Arctic charr [20,21], albeit not with focus on appetite regulation. Consequently, we sequenced the brain transcriptome of fed and feed deprived charr during their natural summertime growth in an attempt to advance the knowledge on global responses to feed deprivation, assess alterations of known central appetite regulators, and to identify possible new actors involved in the control of appetite and energy metabolism in fish.

Results Morphometrics

The experiment was performed on two-year old imma- ture anadromous Arctic charr held under natural photoperiod (24 h light at that time of the year) and ambient water temperature. Growth development during the 4 weeks experimental period was assessed on subsamples of 15 tagged fish per treatment group. Average body mass and condition factor were 98.0 ± 4.2 g and 1.04 ± 0.02 on June 25, 2014, the start of the experiment (T0) (Fig.1). On July 23, the end of the experiment (T1), body mass and condition factor were markedly higher in fed (Fed) than in feed deprived (FDP) charr with 161.4 ± 4.7 g and 1.27 ± 0.01, and 84.4 ± 5.2 g and 0.86 ± 0.02 in the fed and FDP charr, respectively. Average body mass and condition factor of the 5 fish sampled for transcripto- mics fell in the same range as for the tagged fish and were 108.3 ± 10.2 g and 1.05 ± 0.03 at T0and 157.2 ± 8.3 g and 1.25 ± 0.03 (Fed) and 80.6 ± 6.3 g and 0.92 ± 0.04 (FDP) at T1.

De novo transcriptome assembly and annotation

The de novo brain transcriptome assembly produced 49829 contigs with a FPKM greater than 1 for at least one of the 15 samples. Their total length equalled 84028148 base pairs. The N50 (i.e., the contig length that produces half the bases of the assembly) reached 2663 bp (Table1).

The annotation rate reached 85.07% of the 49829 contigs. The Atlantic salmon (Salmo salar) contributed most to the annotation of the Arctic charr brain transcriptome with 57.6% of the contigs annotated on this species (Fig. 2). All other species contributing to the annotation, exceptHomo sapiens,were exclusively teleosts including another salmonid, the rainbow trout (Onco- rhynchus mykiss). One or more GO identifier could be assigned to 13231 out of the 49829 contigs. Different approaches were used to verify the quality of the contigs.

First, the assembly quality and annotation completeness of the transcriptome were assessed by BUSCO analysis.

Out of 4584 single-copy ortholog genes common to Actinopterygii, the assembly was 67.2% complete (2560 complete single-copy BUSCOs and 524 complete dupli- cated BUSCOs), while only 2.8% of contigs were frag- mented (130 BUSCOs) and 30.0% were missing (1370 BUSCOs). Second, a comparison to a phylogenetically

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close species reference proteome was achieved using the Atlantic salmon database from the NCBI. Out of 97555 Salmo salar proteins, 47419 were aligned with at least 80% identity over 80% of their length on our assembly, which corresponded to 12238 Arctic charr contigs.

Third, initial reads were mapped to the contigs in order to validate to what extent the contigs reflected the initial information. The contigs had high realignment rates that ranged between 88 and 89% depending on the sample

(15 libraries). Furthermore, the construction of the RNAseq data set was verified by mapping the reads from an Arctic charr gill transcriptome [22] on our set of contigs. On average, 79.35% of reads from this gill transcriptome (SRA accession: SRX314607) were aligned to the 15 Arctic charr libraries. Finally, both proteins and RNAs from the S. alpinus reference genome (NCBI ID:

12179) [23] were aligned to our set of contigs. Out of 59926 proteins, 49212 (82%) had a hit on the contigs,

Fig. 1Fulton’s condition factor (a) and body mass (b) of tattooed fed (black dots) and feed deprived Arctic charr (white dots) during the experiment.

Charr sampled at T0were measured before distribution to Tank 1 and Tank 2. Dashed line: water temperature.n= 21 per treatment group. Values are shown as mean ± SEM. Different capital and lowercase letters denote differences within treatment group at different time points and differences between treatment groups at the given time point, respectively

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31316 of these proteins shared over 80% similarity and 80% of coverage with the built contigs. Regarding RNAs from the reference genome, 76% (51217 out of the 67196) had a hit on our contigs, with 24432 RNAs hav- ing over 80% of similarity and 80% of coverage with the contigs.

Gene expression patterns and differential gene expression

In order to inspect the overall expression patterns of the 15 samples, a correlation heatmap based on the raw counts of the 49829 contigs possessing a FPKM greater than 1 for at least one sample was drawn (Fig.3). This analysis highlighted that all samples from T0 clustered together and were markedly different from samples

taken at T1. Samples of the fed and feed deprived charr at T1 did not cluster in accordance with the treatment group (Fig.3).

In fed Arctic charr, 2819 contigs differed over time (Table 2). This corresponded to 5.7% of all sequenced contigs. Among these, 1534 contigs were up-regulated, while 1285 were down-regulated.

In feed deprived charr, 4570 (9.2%) contigs differed over time (Table 2). Of these 2616 were up-regulated while 1954 were down-regulated from start to the end of experiment. At T1, only 175 contigs (0.4% of all contigs) were found to be differentially expressed between FDP and Fed charr (Table 2), with 68 contigs being up- regulated and 107 being down-regulated. Matching the six lists of up- and down regulated contigs from the three comparisons in Up-set graphs enabled us to break down the lists of differentially expressed genes and find the intersection points of all three comparisons (Fig. 4).

Over time, 1449 contigs were up-regulated and 825 down-regulated in both FDP and fed charr. Interestingly, at the same time, 1421 contigs were uniquely up- regulated in the FDP versus T0comparison.

In contrast, only 825 contigs were uniquely up- regulated in fed charr over time. Similarly, 1058 contigs, were uniquely down-regulated in FDP charr over time, whereas only 337 contigs were uniquely down-regulated contigs in the Fed versus T0 comparison. Chi-square tests of the 2 × 2 contingency tables of up- and downregulated genes indicated that there was a correlation of the proportion between uniquely and shared differentially expressed contigs and feeding regime over time (up- Table 1General statistics of contigs generated by RNAseq

technology for brain gene expression characterization. Only contigs possessing a FPKM greater than 1 for at least one library were considered for annotation and further expression analysis

Number of base pairs in reads 42491174498

Number of reads 420704698

Number of base pairs in contigs (FPKM> 1) 84028148

Number of contigs (FPKM> 1) 49829

N50 2663

N90 816

Number of putative micro-satellites 34440

Number of putative SNPs 420406

Number of contigs including SNP 39484

Fig. 2Top 20 species contributing the most to best-hit annotations. Best hits were based on all databases involved in the annotation process.

Yellow: teleosts, orange: salmonids, blue: mammals

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regulated: X-squared = 387.74, p-value < 0.0001, down- regulated: X-squared = 153.5, p-value < 0.0001) (Fig. 4).

Only five contigs were up-regulated in the Fed vs T0

comparison whilst down regulated in the FDP versus T0

comparison. No contigs were down-regulated in the Fed vs T0and at the same time up-regulated in the FDP vs T0comparison (Fig.4).

GO enrichment analysis for biological processes (BP) was employed to study the biological functions of the differentially expressed contigs. This would depict global effects of season and feed deprivation during summer on the charr’s brain transcriptome.

Contigs that were only up-regulated in fed fish over time were associated with biological processes that in- cluded oxygen transport and protein related biological processes (Table 3). Down-regulated contigs in fed fish contributed amongst others to terms like ion transport, protein complex assembly (Table4), and“feeding behaviour” (Additional file 4: Table S2). In both fed and feed deprived charr, we found that up-regulated contigs over time were involved in basic cell metabolism processes such as DNA replication and RNA metabolism (Table5)

. Contigs that were down-regulated over time regardless of the feeding regime were amongst others related to ion transport, protein related processes and wnt signaling (Table6, Additional file6: Table S4). Feed deprivation over time led to an up-regulation of contigs relating to processes such as catabolism, apoptosis and immune system (Table 7) and a down-regulation of e.g. oxygen transport (Table 8). An up-regulation of contigs that take part in catabolic and apoptotic processes and down-regulation of oxygen transport was also found in the endpoint comparison between FDP and fed charr (Additional file 9: Table S7, Table9, Additional file10: Table S8).

In an attempt to unravel whether central appetite signalling pathways in the charr brain transcriptome were modulated by feed-deprivation, we screened the lists of differentially expressed contigs systematically for candidate genes that have previously been demonstrated to be involved in the regulation of appetite and energy homeostasis in fish [9, 10]. Further, due to a strong effect of season and temperature seen on the brain transcriptome (Fig. 3), genes involved in seasonal rhythms were in- cluded in the search. Lastly, we searched the dataset for possible new actors involved in the regulation of energy homeostasis and food intake, not previously described in fish.

Several candidate genes possibly involved in seasonality and food intake control in fish were found to be differentially expressed between one or several comparisons and are displayed in Table10. Differences in expression were mainly found between the T1and T0for FDP and/or fed

Fig. 3Correlation heatmap based on raw counts of the 49829 contigs possessing a FPKM greater than 1 for at least one sample. X01-X15: number of sampled fish during the experiment. T0: fish sampled at the start of the experiment (June 25). FED: fed fish sampled at the end of the experiment (July 23). FDP: feed deprived fish sampled at the end of experiment (July 23)

Table 2Number of up- and down-regulated transcripts in the different comparisons returned by EdgeR analysis. Cut-off at FDR < 0.05 and at LogFC 0.5/−0.5

T1_Fed versus T0 T1_FDP versus T0 T1_ FDP versus T1_Fed

Up Down Up Down Up Down

1534 1285 2616 1954 68 107

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fish. Deiodinase 2b (Dio2b) was also two-fold lower expressed in FDP charr than in fed charr at the end of the experiment (Fig. 5). The lists of top annotated genes that were found differentially expressed in the different comparisons (Additional file 11: Table S9, Additional file 12: Table S10, Additional file 13: Table S11, Additional file 14: Table S12, Additional file 15:

Table S13, and Additional file 16: Table S14) were screened for genes that, based on prior knowledge from mammals, are known to be involved in energy homeostasis and control of food intake. Here, the gene nerve growth factor inducible (VGF) was found as a new candidate that may be involved the regulation of appetite and energy metabolism in fish (Fig.6).

Fig. 4Number of up- and down-regulated transcripts in the different pairwise comparisons. The total number of differentially expressed transcripts for the different comparisons are presented as horizontal bars. Vertical bars represent lists of differentially expressed genes that were only found in one specific comparison or shared by two or more comparisons, indicated by dots and dots connected by lines, respectively, below

Table 3Biological processes enriched by up-regulated contigs only found in Fed versus T0(see yellow fraction in Venn diagram Additional file1: Fig. S1). Terms sorted by the number of contributing contigs

GO.ID Term Annotated Significant Expected p-value

GO:0006810 Transport 1483 25 14.54 0.0026

GO:0051234 establishment of localization 1484 25 14.55 0.0026

GO:0051179 Localization 1501 25 14.71 0.0031

GO:0044765 single-organism transport 883 19 8.66 0.0006

GO:1902578 single-organism localization 893 19 8.75 0.0007

GO:0015669 gas transport 16 10 0.16 3.20E-17

GO:0015671 oxygen transport 16 10 0.16 3.20E-17

GO:0006457 protein folding 104 5 1.02 0.0034

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Data release

Our raw RNA-seq data are available in the SRA database [24] under accession number SRP151983. In addition, we specifically developed an interface for the present de novo transcriptome. The entire dataset can be browsed at the following URL: http://ngspipelines2.toulouse.inra.

fr:9002/ the main menu enables the user to enter the contigs, SNPs and download sections. The contig section main page displays three blocks providing (1) statistics upon the assembly and the annotations, (2) information and access to statistical analysis tools (digital differential display, Venn diagram) on the library and (3) a table that displays data of the contigs that are of the user’s stron- gest interest. Once a contig has been selected, the user can view its general annotation including Gene Ontol- ogy, check its sequence for possible open reading frames, visualize the annotation location in the“jbrowse view”and scan the alignment coverage along the contig for each library. All these features are accessible through the menu located at the top of the page. The SNP section has also a main page presenting some statistics and the favourite table. Once a variable locus is selected, the user can access general information such as the list of al- leles and the flanking sequences, allele information with the allele count graph and table for each library and the corresponding functional annotation.

Discussion

Feed deprived and fed charr showed a diverging development of weight and condition

The strong increase in K and body mass observed over the 4 weeks period in the fed charr (Fig.1) was consistent with the high food intake and growth of anadromous Arc- tic charr during summer [17, 25]. The FDP charr under- went a strong mobilization of energy reserves during the experiment resulting in a markedly lower K and body mass in FDP charr than in fed charr at T1(Fig.1). This

effect of treatment was also evident in the 5 fish from each treatment sampled for transcriptome analyses.

A reliable de novo brain transcriptome

Out of the 49829 generated contigs, more than 85%

were annotated, mainly based on Atlantic salmon (Fig.

2). This was expected as Atlantic salmon was phylogenetically the closest related species with a sequenced genome at the time the charr de novo transcriptome was built (spring 2016) [26]. Besides other teleost species contributing to the annotation, Homo sapiens also gave hits: the human genome is well characterized and might have brought annotations of genes that, so far, have only been described in humans.

Even if it is known for its complex transcriptomic signa- ture, the brain alone cannot be representative of the entire diversity of the Arctic charr transcriptome. Thus, a completeness of 67% (BUSCO analysis) confirmed that the assembly produced correct contigs. Indeed other fish transcriptome de novo assemblies brought results in the same order of magnitude with 70.2% completeness for the gut tench (Tinca tinca) [27] and 64% for 4 combined tis- sues of the noble crayfish (Astacus astacus) [28]. In addition, (1) the comparison to the phylogenetically close Atlantic salmon reference proteome, (2) the calculation of the realignment rates and (3) the mapping of an Arctic charr public raw data using a de novo assembly from short read RNA-seq data on our dataset also confirmed a high completeness of the built transcriptome.

Global brain gene expression patterns differ over time while feed deprivation has a moderate impact

The correlation heatmap highlighted that charr brain transcriptomes from T0 clustered together and were markedly different from the ones sampled at T1. Samples of the fed and FDP charr at T1did not cluster in accordance with the treatment group. These results stand in contrast with the strong divergence in K and weight Table 4Biological processes enriched by down-regulated contigs only found in Fed versus T₀(see yellow fraction in Venn diagram Additional file1: Figure S1) Terms sorted by the number of contributing contigs

GO:0006811 ion transport 532 10 3.48 0.0019

GO:0006461 protein complex assembly 173 4 1.13 0.0258

GO:0070271 protein complex biogenesis 173 4 1.13 0.0258

GO:0065003 macromolecular complex assembly 189 4 1.24 0.03419

GO:0071822 protein complex subunit organization 189 4 1.24 0.03419

GO:0009966 regulation of signal transduction 212 4 1.39 0.04877

GO:0010646 regulation of cell communication 213 4 1.39 0.04947

GO:0023051 regulation of signalling 213 4 1.39 0.04947

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Table 5Biological processes enriched by up-regulated contigs found in Fed versus T₀and FDP versus T₀(see white fraction in Venn diagram Additional file1: Figure S1). Terms sorted by the number of contributing contigs

GO:0019438 aromatic compound biosynthetic process 782 28 16 0.0021

GO:0018130 heterocycle biosynthetic process 796 28 16.28 0.0027

GO:1901362 organic cyclic compound biosynthetic process 803 28 16.43 0.0031

GO:0034654 nucleobase-containing compound biosynthetic process 750 25 15.34 0.0092

GO:0080090 regulation of primary metabolic process 622 24 12.72 0.0017

GO:0031323 regulation of cellular metabolic process 632 24 12.93 0.0021

GO:0019222 regulation of metabolic process 641 24 13.11 0.0025

GO:0019219 regulation of nucleobase-containing compound metabolic process 577 23 11.8 0.0014

GO:0051171 regulation of nitrogen compound metabolic process 587 23 12.01 0.0017

GO:0060255 regulation of macromolecule metabolic process 619 23 12.66 0.0034

GO:0006355 regulation of transcription, DNA-templated 568 22 11.62 0.0025

GO:1903506 regulation of nucleic acid-templated transcription 568 22 11.62 0.0025

GO:2001141 regulation of RNA biosynthetic process 569 22 11.64 0.0026

GO:0051252 regulation of RNA metabolic process 572 22 11.7 0.0028

GO:0010556 regulation of macromolecule biosynthetic process 580 22 11.87 0.0033

GO:2000112 regulation of cellular macromolecule biosynthetic process 580 22 11.87 0.0033

GO:0031326 regulation of cellular biosynthetic process 581 22 11.89 0.0034

GO:0009889 regulation of biosynthetic process 582 22 11.91 0.0034

GO:0010468 regulation of gene expression 583 22 11.93 0.0035

GO:0006351 transcription, DNA-templated 630 22 12.89 0.0087

GO:0097659 nucleic acid-templated transcription 630 22 12.89 0.0087

GO:0032774 RNA biosynthetic process 633 22 12.95 0.0092

GO:0051276 chromosome organization 94 7 1.92 0.003

GO:0006325 chromatin organization 72 6 1.47 0.0034

GO:0006955 immune response 50 5 1.02 0.0034

GO:0002376 immune system process 52 5 1.06 0.004

GO:0016571 histone methylation 11 3 0.23 0.0012

GO:0018022 peptidyl-lysine methylation 11 3 0.23 0.0012

GO:0034968 histone lysine methylation 11 3 0.23 0.0012

GO:0018205 peptidyl-lysine modification 22 3 0.45 0.0097

GO:0016569 covalent chromatin modification 23 3 0.47 0.011

GO:0016570 histone modification 23 3 0.47 0.011

GO:0019882 antigen processing and presentation 26 3 0.53 0.0154

GO:0006479 protein methylation 27 3 0.55 0.0171

GO:0008213 protein alkylation 27 3 0.55 0.0171

GO:0033993 response to lipid 28 3 0.57 0.0189

GO:0043401 steroid hormone mediated signaling pathway 28 3 0.57 0.0189

GO:0048545 response to steroid hormone 28 3 0.57 0.0189

GO:0071383 cellular response to steroid hormone stimulus 28 3 0.57 0.0189

GO:0071396 cellular response to lipid 28 3 0.57 0.0189

GO:0014070 response to organic cyclic compound 29 3 0.59 0.0208

GO:0071407 cellular response to organic cyclic compound 29 3 0.59 0.0208

GO:0009725 response to hormone 31 3 0.63 0.0248

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between the fed and FDP charr. However, feed deprivation may only have affected very specific processes in the brain transcriptome leaving the overall expression pattern less strongly affected.

Seasonal changes alone (including the increased water temperature), or in combination with feed deprivation, induced a high number of differing contigs from start to end of the experiment, compared to the minor differences observed between the treatment groups at the endpoint. This pattern depicts strong shifts in the charr brain gene expression over a 4 weeks period during summer, which occurred regardless of feeding regime (Fig.4) . The results underline that seasonal processes, including changes in water temperature and summer growth, have much stronger effects on the brain transcriptome in the charr than 1 month of feed deprivation during the feeding season. However, the two times higher number of differentially expressed contigs over time in the feed deprived charr than in the fed conspecifics may suggest an enhancement of seasonal differences by feed deprivation (Table 2). The large changes seen over time correspond with an earlier transcriptome study on different Arctic

charr morphs, where time, in that case during early development, was the most important factor for differentially expressed transcripts [29].

In addition, the Up-set graphs (Fig.4) showed that a larger fraction of both up- and down-regulated contigs differed only in response to feed deprivation. On the other hand, most of the differences found in the fed fish were at the same time found in the FDP charr. These shared differences over time depict robust seasonal processes that remain unaffected by feed availability.

Food deprivation partially offsets increase in brain metabolism during summer growth

Feeding specific differences over time

Up-regulated contigs over time denoted foremost oxygen transport (genes encoding for several haemoglobin (Hb) subunits) and protein related biological processes (Table 3). Brain Hb mRNA has been found in rodents and humans [30] and it has been suggested that neural haemoglobin may facilitate oxygen transport in neurons [31], but the exact mechanism remains unknown. In addition, it cannot be ruled that the cDNA library may Table 5Biological processes enriched by up-regulated contigs found in Fed versus T₀and FDP versus T₀(see white fraction in Venn diagram Additional file1: Figure S1). Terms sorted by the number of contributing contigs(Continued)

GO:0009755 hormone-mediated signaling pathway 31 3 0.63 0.0248

GO:0032870 cellular response to hormone stimulus 31 3 0.63 0.0248

GO:0018193 peptidyl-amino acid modification 34 3 0.7 0.0316

GO:0006334 nucleosome assembly 35 3 0.72 0.0341

GO:0016568 chromatin modification 35 3 0.72 0.0341

GO:0031497 chromatin assembly 35 3 0.72 0.0341

GO:0034728 nucleosome organization 35 3 0.72 0.0341

GO:0006323 DNA packaging 36 3 0.74 0.0366

GO:0006333 chromatin assembly or disassembly 37 3 0.76 0.0393

Table 6Biological processes enriched by down-regulated contigs found in Fed versus T0and FDP versus T0(see white fraction in Venn diagram Additional file1: Figure S1). Terms sorted by the number of contributing contigs

GO:0044699 single-organism process 3629 54 45.37 0.04

GO:0006811 ion transport 532 12 6.65 0.0322

GO:0006470 protein dephosphorylation 126 6 1.58 0.0048

GO:0016311 dephosphorylation 162 6 2.03 0.0156

GO:0015672 monovalent inorganic cation transport 175 6 2.19 0.0219

GO:0006813 potassium ion transport 88 4 1.1 0.024

GO:0051258 protein polymerization 36 3 0.45 0.01

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have contained RNA from non-neuronal tissue including blood vessels and veins, e.g. derived from the highly vascularised saccus vasculosus. The metabolic rate of ectotherms is directly linked to ambient temperature. Specifically, the temperature of optimal growth performance of Arctic charr from North- Norway has been shown to be 14 °C [32]. The increase in water temperature by almost 10 °C during our study may have involved an increase in metabolic rate of Arctic charr. Hence, the observed increase in expression of genes encoding oxygen transporters

may have occurred in order to meet increased oxygen demands at higher temperatures.

Furthermore, the feeding related terms such as “feeding behaviour” and“negative regulation of appetite” appeared in the list of GO terms of down-regulated contigs. The contig that contributed to these GO terms was the anorexigenic neuropeptideCART, thus suggest- ing an increase in appetite in the fed charr over time (Additional file4: Table S2). The decrease inCART expression is discussed in detail in the paragraph on candidate appetite regulators.

Table 7Biological processes enriched by up-regulated contigs only found in FDP versus T₀(see blue fraction in Venn diagram Additional file1: Figure S1). Terms sorted by the number of contributing contigs

GO:0065007 biological regulation 2021 62 47.66 0.00925

GO:0050789 regulation of biological process 1973 61 46.53 0.00837

GO:0050794 regulation of cellular process 1934 59 45.61 0.01308

GO:0006725 cellular aromatic compound metabolic process 1375 42 32.43 0.03914

GO:0090304 nucleic acid metabolic process 1097 35 25.87 0.03448

GO:0016070 RNA metabolic process 884 30 20.85 0.02407

GO:0060255 regulation of macromolecule metabolic process 619 22 14.6 0.03315

GO:0080090 regulation of primary metabolic process 622 22 14.67 0.03473

GO:0031323 regulation of cellular metabolic process 632 22 14.9 0.04041

GO:0019222 regulation of metabolic process 641 22 15.12 0.04609

GO:0051252 regulation of RNA metabolic process 572 20 13.49 0.04778

GO:0006396 RNA processing 189 10 4.46 0.01367

GO:0006955 immune response 50 7 1.18 0.00015

GO:0002376 immune system process 52 7 1.23 0.0002

GO:0008219 cell death 58 6 1.37 0.00231

GO:0016265 death 58 6 1.37 0.00231

GO:0010941 regulation of cell death 45 5 1.06 0.00391

GO:0042981 regulation of apoptotic process 45 5 1.06 0.00391

GO:0043067 regulation of programmed cell death 45 5 1.06 0.00391

GO:0006915 apoptotic process 57 5 1.34 0.0107

GO:0012501 programmed cell death 57 5 1.34 0.0107

GO:0048518 positive regulation of biological process 67 5 1.58 0.02048

GO:0006397 mRNA processing 68 5 1.6 0.0217

GO:0016071 mRNA metabolic process 79 5 1.86 0.03824

GO:0019882 antigen processing and presentation 26 4 0.61 0.00297

GO:0051726 regulation of cell cycle 40 4 0.94 0.01409

GO:0015074 DNA integration 55 4 1.3 0.04013

GO:0010942 positive regulation of cell death 13 3 0.31 0.0031

GO:0043065 positive regulation of apoptotic process 13 3 0.31 0.0031

GO:0043068 positive regulation of programmed cell death 13 3 0.31 0.0031

GO:0007050 cell cycle arrest 18 3 0.42 0.0081

GO:0045786 negative regulation of cell cycle 21 3 0.5 0.01254

GO:0008380 RNA splicing 26 3 0.61 0.0225

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Table 8Biological processes enriched by down-regulated contigs only found FDP versus T₀(see blue fraction in Venn diagram Additional file1: Figure S1). Terms sorted by the number of contributing contigs

GO:0006807 nitrogen compound metabolic process 1736 66 53.76 0.03251

GO:1901360 organic cyclic compound metabolic process 1400 55 43.36 0.0299

GO:0009058 biosynthetic process 1439 55 44.57 0.04759

GO:1901576 organic substance biosynthetic process 1374 54 42.55 0.03127

GO:0044249 cellular biosynthetic process 1358 53 42.06 0.03699

GO:0006725 cellular aromatic compound metabolic process 1375 53 42.58 0.0453

GO:1901564 organonitrogen compound metabolic process 620 28 19.2 0.02644

GO:0016043 cellular component organization 466 23 14.43 0.01725

GO:0071840 cellular component organization or biogenesis 499 23 15.45 0.03484

GO:1901566 organonitrogen compound biosynthetic process 451 21 13.97 0.039

GO:0006508 proteolysis 298 18 9.23 0.00495

GO:0022607 cellular component assembly 222 13 6.88 0.02012

GO:0034622 cellular macromolecular complex assembly 127 12 3.93 0.00054

GO:0007017 microtubule-based process 132 12 4.09 0.00076

GO:0006082 organic acid metabolic process 226 12 7 0.04764

GO:0019752 carboxylic acid metabolic process 226 12 7 0.04764

GO:0043436 oxoacid metabolic process 226 12 7 0.04764

GO:0006520 cellular amino acid metabolic process 126 10 3.9 0.00554

GO:0051258 protein polymerization 36 9 1.11 1.00E-06

GO:0043623 cellular protein complex assembly 76 9 2.35 0.00052

GO:0030163 protein catabolic process 118 8 3.65 0.02963

GO:1902582 single-organism intracellular transport 88 7 2.73 0.01889

GO:0044257 cellular protein catabolic process 100 7 3.1 0.03502

GO:0051603 proteolysis involved in cellular protein catabolic process 100 7 3.1 0.03502

GO:1901605 alpha-amino acid metabolic process 58 5 1.8 0.03302

GO:0006270 DNA replication initiation 4 4 0.12 9.00E-07

GO:0006261 DNA-dependent DNA replication 5 4 0.15 4.40E-06

GO:0006839 mitochondrial transport 16 4 0.5 0.00121

GO:0009069 serine family amino acid metabolic process 20 4 0.62 0.00293

GO:0008652 cellular amino acid biosynthetic process 31 4 0.96 0.01461

GO:1901607 alpha-amino acid biosynthetic process 31 4 0.96 0.01461

GO:0071103 DNA conformation change 44 4 1.36 0.04628

GO:0009070 serine family amino acid biosynthetic process 7 3 0.22 0.00094

GO:0031032 actomyosin structure organization 7 3 0.22 0.00094

GO:0000278 mitotic cell cycle 14 3 0.43 0.00828

GO:0006720 isoprenoid metabolic process 14 3 0.43 0.00828

GO:0008299 isoprenoid biosynthetic process 14 3 0.43 0.00828

GO:1903047 mitotic cell cycle process 14 3 0.43 0.00828

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Feed deprivation specific differences over time

In the FDP charr, up-regulated contigs were associated with processes such as catabolism, apoptosis, and immune function (Table 7). In contrast, no such trends were found in brain transcriptome analyses of 21-day feed-deprived zebrafish [33]. The finding that contigs relating to apoptosis were increased is puzzling, given the consensus that the brain is well protected from starva- tion in both mammals [34] and fish [35]. However, in mammals, there is a debate on to what extend feed deprivation may initiate a degeneration of the central nervous system, as different studies have given indica- tion for both absence [36] and presence [37] of autoph- agy in the brain of feed deprived mice. Further experiments are needed to test whether the observed up-regulation of contigs involved in apoptosis were a sign of neuronal degradation in charr.

Furthermore, we found the GO term “ketone body catabolic process”, comprising the gene encoding 3- oxoacid CoA transferase, to be up-regulated in the feed deprived charr, pointing towards an increase in ketone catabolic activity from start to the end of experiment (Additional file7: Table S5). This is in line with previous studies on Atlantic salmon and rainbow trout, where ketone bodies were found to serve as an important energy source for the brain when food is absent [38,39].

In contrast to the fed charr, contigs pertaining to oxygen transport were down-regulated in feed deprived charr (Table 8). This finding is in agreement with the down-regulation of transcripts related to oxygen transport in response to feed deprivation previously seen in Atlantic salmon liver transcriptome [40], rainbow trout liver transcriptome [41] and in zebrafish brain transcriptome [33]. The lower expression of Hb in feed deprived Table 8Biological processes enriched by down-regulated contigs only found FDP versus T₀(see blue fraction in Venn diagram Additional file1: Figure S1). Terms sorted by the number of contributing contigs(Continued)

GO:0015669 gas transport 16 3 0.5 0.01218

GO:0015671 oxygen transport 16 3 0.5 0.01218

Table 9Biological processes enriched by down-regulated contigs comparing feed deprived versus fed charr at end of experiment.

Terms sorted by the number of contributing contigs

GO:0006810 transport 1483 15 8.85 0.02027

GO:0051234 establishment of localization 1484 15 8.85 0.02039

GO:0051179 localization 1501 15 8.96 0.0225

GO:0015669 gas transport 16 11 0.1 3.50E-22

GO:0015671 oxygen transport 16 11 0.1 3.50E-22

GO:0006259 DNA metabolic process 228 6 1.36 0.00214

GO:0006260 DNA replication 113 5 0.67 0.00052

GO:0006270 DNA replication initiation 4 4 0.02 1.10E-09

GO:0006261 DNA-dependent DNA replication 5 4 0.03 5.50E-09

GO:0051258 protein polymerization 36 4 0.21 5.60E-05

GO:0043623 cellular protein complex assembly 76 4 0.45 0.00103

GO:0034622 cellular macromolecular complex assembly 127 4 0.76 0.00666

GO:0022607 cellular component assembly 222 4 1.32 0.04232

GO:0007017 microtubule-based process 132 3 0.79 0.04352

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charr may be related to a metabolic suppression in FDP fish in order to save energy when feed is absent. Brain metabolic suppression, indicated by a reduction of glu- cose oxidation has previously been observed in feed deprived rainbow trout [39]. However, these feed deprived rainbow trout showed a decrease in hexokinase and 6- phosphofruktokinase activities in the brain. In contrast,

expression of these glycolytic enzymes did not differ between fed and FDP charr in the present study.

Differences between fed and feed deprived charr at the end of the experiment

At T1, there was an up-regulation of contigs involved in biological processes related to apoptosis by feed Table 10Differentially expressed candidate genes involved in food intake control and seasonality

T1_Fed vs T0 T1_FDP vs T0 T1_FDP vs T1_Fed

Gene Name ContigID Accession No. LOG FC Pvalue LOG FC Pvalue LOG FC Pvalue

Apelin receptor A Fishapp_brain_apja.2.3 NM_001140368.1 0.99 0.003 – – – –

Cocaine and amphetamine regulated transcript

Fishapp_brain_contig_33002 NM_001146680.1 −0.66 < 0.001 −0.33 0.006 – –

Cholecystokinin Fishapp_brain_contig_18503 NM_001139522.1 – – – – – –

Fishapp_brain_contig_21023 – – – – – –

Fishapp_brain_contig_17948 – – −0.37 0.003 – –

Corticotropin-releasing factor Fishapp_brain_contig_16188 NM_001124627.1 – – −0.55 0.008 – – Deiodinase 2b Fishapp_brain_contig_18436 NM_001124268.1 −0.60 0.003 −1.79 < 0.001 −1.19 < 0.001

Fishapp_brain_contig_15175 −1.21 > 0.001 −2.02 < 0.001 −0.81 < 0.001

Insulin like growth factor 1 Fishapp_brain_IGF1 GU933431.1 – – −0.81 0.003 – –

Leptin Fishapp_brain_lepb1 JX131305.1 1.701 < 0.001 1.37 < 0.001 – –

Neuropeptide Y Fishapp_brain_npy NM_001146681.1 −0.55 < 0.001 – – – –

Proopiomelanocortin Fishapp_brain_contig_04399 XM_024143555.1 – – 5.99 0.001 – –

Tachykinin 1 Fishapp_brain_contig_09262 XM_023974799.1 – – −0.51 0.002 – –

Genes found in the transcriptome, but no difference in expression:

Apelin,Agouti related peptide,Arginine vasotocin,Galanin,Melanin concentrating hormone,Peptide YY,Thyroid releasing hormone Genes searched, but not present in the transcriptome:

Ghrelin,Kisspeptin,Leptin receptor,Melanocortin receptor 4,Obestatin,Octadecaneuropeptide,Orexin,Pituitary adenylate cyclase-activating polypeptide,Prolactin releasing peptide,Secretoneurin

Fig. 5Log counts per million reads (logCPM) for contigs corresponding to Dio2b in the three treatment groups. Data are presented as box and whisker plots with median, 25th and 75th percentiles and 1.5 * interquartile range. In addition, individual data points are indicated within the plot

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deprivation as well as a down-regulation of biological processes related to oxygen transport (Additional file 9:

Table S7, Table 9, Additional file 12: Table S10). These findings further support the possibility that brain metabolic processes may have been partly impaired by feed deprivation, as the changes were both visible over time and between the fed and FDP fish at the end of the experiment.

Differences over time regardless of feeding regime

Over time, there was an up-regulation of contigs involved in biological processes such as DNA replication, RNA metabolism, response to steroid hormones and immune response (Table 5). This may indicate that basic cell metabolic processes such as cell proliferation and neuronal development were positively affected by seasonal growth and temperature. At the same time, there was a down-regulation of contigs affecting biological processes such as ion transport, protein related processes and wnt signalling from start to end of the experiment (Additional file6: Table S4).

Interestingly, in adult zebrafish, activation and deacti- vation of wnt signalling in a sequential manner has been shown to accommodate proliferation and differentiation of progenitor cells in the hypothalamus [42]. Further- more, the finding that wnt signalling in the ARC was stimulated by leptin in mouse [43] and by both leptin and long photoperiod in the seasonal Djungarian ham- ster (Phodopus sungorus) [44] have triggered a discussion for a role of hypothalamic wnt signalling in the seasonal control of energy balance [45]. Yet, we only found

differences in expression forWNT4, and the question of a possible seasonal related function of wnt signalling in charr requires further study.

Taken together, the results from the GO analyses indicate that the enforced energy perturbation by feed deprivation may have affected several metabolic processes in the brain at the mRNA level. Yet, most elementary biological processes, including cell div- ision processes and immune responses, differed similarly over time in both fed and feed deprived charr.

Effect of feed deprivation on candidate genes involved in the regulation of appetite and energy metabolism in fish Expression patterns of known anorexigenic and orexigenic neuropeptides do not indicate an amplified hunger signalling in feed deprived charr

Despite the lower weight andKin feed deprived charr, expression of central appetite regulators did not reflect the feeding regime of the fish (Table 10). Several genes differed in one or both feeding regimes over time, but none between FDP and fish at T1. There was a lower expression of the anorexigenic CARTat T1in both fed (LogFC =− 0.66) and FDP (LogFC =−0.33) charr than in fed charr at T0(Table 10). If CART exhibits an anorexigenic function in charr, the lower expression of CART in both fed and FDP fish over time points towards a seasonal increase in hunger signalling in both treatment groups, but no response to feed deprivation. This corresponds to the lack of responses in hypothalamic CART expression seen in previous long-term feed deprivation studies with rainbow

Fig. 6Log counts per million reads (logCPM) for the contig vgf3.3 in the three treatment groups. Data are presented as box and whisker plots with median, 25th and 75th percentiles and 1.5 * interquartile range. In addition, individual data points are indicated within the plot

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trout (4 months) and Arctic charr (4 weeks) [19, 46]. In contrast, brain CART expression decreased in zebrafish after 3 days feed deprivation [47], in Atlantic salmon after 6 days of feed deprivation [48] and in Atlantic cod (Gadus morhua) after 7 days feed deprivation [49].

Being described as a potent satiety signal in mammals and several fish species, hypothalamic POMCAhad, un- expectedly, markedly (LogFC = 5.99) higher expression levels in FDP charr at T1than in charr sampled at T0. In contrast to the results seen for CART, this finding may suggest a decrease in hunger signalling in FDP charr over time. This result corresponds to an increased hypothalamic expression of POMCA1 and POMCB in rainbow trout after 4 months of feed deprivation [46]. In another experiment with rainbow trout, hypothalamic POMCA1 was down-regulated after 28 days of feed deprivation [50]. However, POMCA paralogues could not be distinguished in the present study, and a possible subfunctionalisation of these in the charr, which may be reflected in different responses to feed deprivation, cannot be excluded.

Putatively anorexigenic corticotrophin releasing factor (CRF) expression was lower in FDP charr at T1than at T0 (LogFC −0.55). Previously, reduced CRF brain expression levels were observed in goldfish (Carassius aur- atus) after 7 days of feed deprivation [51] whereas no changes were observed after short (7 days)- and long- term (4 weeks) feed deprivation in charr [19] or after long-term feed deprivation in rainbow trout [46].

In rat, the preprotachykinin 1 (PPT) protein, encoded by theTAC1gene, has been shown to be negatively regulated by ghrelin and high fat diets, and hence is as- sumed to be involved in regulating adiposity in rodents [52]. In goldfish, the post-prandial increase of hypothalamic expression of μ-PPThas led to the suggestion that μ-PPT may signal satiety [53]. In the present study, brain TAC1 expression was lower expressed in FDP charr at T1than at T0(LogFC−0.51).

The function of locally produced LEP in the brain is still a matter of debate, both in mammals and in fish [54–56], and results from other studies have so far not provided evidence for a role of central LEP in appetite regulation in fish [18, 56]. This study revealed a higher LEP expression in both fed (LogFC 1.7) and FDP fish (LogFC 1.3) at T1compared with T0fish. The lack of differences between fed and FDP charr at T1, despite a pro- found difference in condition factor (Fig. 1), may indicate that central leptin expression is not linked to adiposity. On the other hand, LEPA1 was found to be more highly expressed in the hypothalamus of hyperphagic charr in July than of anorexic charr in May and January [18].

Previous studies have shown a conserved orexigenic function of NPY in fish [12,57,58].NPYexpression was

lower in fed fish at T1compared to T0 (LogFC −0.55) while no difference was seen between FDP andT0. As such, based on the brain transcriptome, no hunger signalling by up-regulation of NPYcould be found in the FDP fish. This finding is in accordance with the lack of responses seen in other feed deprivation studies with in Atlantic cod [49], Atlantic salmon [48], charr [19] and rainbow trout [46].

Apelin is considered another potent orexigenic actor in fish [59, 60]. Our data did not reveal an effect of long- term feed deprivation on apelin expression. Brain expression of apelin receptor (APJA), was, however, higher in fed fish at T1than in fed fish at T0(LogFC = 0.99) but not different between FDP and fed fish at T0.

In summary, the results of the present study did not show expected responses to feed deprivation in the expression of candidate genes involved in appetite regulation in fish. This result does not necessarily contradict an appetite regulatory role of these actors in this fish. In a previous study with charr, no differences in the hypothalamic expression of AgRP, MC4R, CRF, NPY, CART, POMCs and LEPR were seen between fed and 4 weeks feed deprived fish, whereas the expression of, CART, MC4R and AgRP changed in feed deprived upon re- feeding or exposure to feed flavour for 1 or 5 h [19]. This indicates that changes in gene expression are more likely to be seen during transition stages than during steady- state situations.

Such paradoxical results may be interpreted as an adaptation in high-latitude fish to save energy by redu- cing feed searching behaviour when feed is absent.

Genes related to energy metabolism and seasonality Insulin-like growth factor 1 (IGF1) is key growth regulating hormone in vertebrates, and plasma levels of IGF1 usually correlate positively with growth in fish [61]. Ac- cordingly, it has been shown that plasma IGF1 levels vary proportionally with increases and decreases in feeding rate in Arctic charr [62]. Furthermore, hypothalamic IGF1 expression was reduced by 1 month feed deprivation resulting in a positive correlation also between hypothalamic IGF1 expression and K of fed and feed deprived fish [19]. In the present study, IGF1 expression was, as expected, downregulated in feed deprived charr at T1 compared to fed charr at T0. However, no difference was found in central IGF1 expression between fed and feed deprived charr at T1, despite the huge difference in Kbetween feed deprived and fed charr at T1 (Fig. 1). This discrepancy in results between studies on Arctic charr may relate to the fact that hypothalamic IGF1 expression was measured in the former study by Striberny and Jørgensen [19], while whole brain IGF1 expression was measured in the present study.